Design Of A Large Scale Constrained Optimization Algorithm And Its Application To Digital Human Simulation
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Digital Human Modeling
Author: Vincent G. Duffy
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-06-24
This book constitutes the refereed proceedings of the Third International Conference on Digital Human Modeling, ICDHM 2011, held in Orlando, FL, USA in July 2011. The 58 revised papers presented were carefully reviewed and selected from numerous submissions. The papers accepted for presentation thoroughly cover the thematic area of anthropometry applications, posture and motion modeling, digital human modeling and design, cognitive modeling, and driver modeling.
Design of a Large-scale Constrained Optimization Algorithm and Its Application to Digital Human Simulation
A new optimization algorithm, which can efficiently solve large-scale constrained non-linear optimization problems and leverage parallel computing, is designed and studied. The new algorithm, referred to herein as LASO or LArge Scale Optimizer, combines the best features of various algorithms to create a computationally efficient algorithm with strong convergence properties. Numerous algorithms were implemented and tested in its creation. Bound-constrained, step-size, and constrained algorithms have been designed that push the state-of-the-art. Along the way, five novel discoveries have been made: (1) a more efficient and robust method for obtaining second order Lagrange multiplier updates in Augmented Lagrangian algorithms, (2) a method for directly identifying the active constraint set at each iteration, (3) a simplified formulation of the penalty parameter sub-problem, (4) an efficient backtracking line-search procedure, (5) a novel hybrid line-search trust-region step-size calculation method. The broader impact of these contributions is that, for the first time, an Augmented Lagrangian algorithm is made to be competitive with state-of-the-art Sequential Quadratic Programming and Interior Point algorithms. The present work concludes by showing the applicability of the LASO algorithm to simulate one step of digital human walking and to accelerate the optimization process using parallel computing.
Introduction to Digital Human Modeling
Introduction to Digital Human Modeling bridges the gap in current literature by providing a comprehensive resource on digital human modeling for beginners and researchers. The content includes step-by-step procedures for building a digital human model, fundamental human kinematics and dynamics, advanced topics such as motion prediction and injury prevention, and industrial applications. The book covers theoretical concepts and experimental validation, including human anatomy, degrees of freedom, skeletal and musculoskeletal modeling, equations of motion, reach envelopes, lifting prediction, muscle fatigue model, and injury analysis. It teaches readers how to build simulation-based human models, set up equations of motion, analyze human biomechanics, and utilize simulations and experiments to study worker injuries. Furthermore, the book introduces both fundamental and advanced digital human modeling methods and optimization techniques aimed at improving performance and preventing injuries in manual material handling, as well as addressing lifting and gait biomechanics and ergonomics. - Step-by-step procedures for building a digital human model - Validation of predicted human motion using simulations and experiments - Application of formulated optimization techniques for dynamic human motion prediction - Hybrid musculoskeletal motion prediction and fatigue modeling